Institute of Physical and Theoretical Chemistry, Technische Universität Braunschweig, Gaußstr. 17, Braunschweig 38106, Germany.
J Phys Chem B. 2024 Nov 28;128(47):11597-11606. doi: 10.1021/acs.jpcb.4c05645. Epub 2024 Nov 17.
Quantum-chemical fragmentation methods offer an attractive approach for the accurate calculation of protein-ligand interaction energies. While the molecular fractionation with conjugate caps (MFCC) scheme offers a rather straightforward approach for this purpose, its accuracy is often not sufficient. Here, we upgrade the MFCC scheme for the calculation of protein-ligand interactions by including many-body contributions. The resulting fragmentation scheme is an extension of our previously developed MFCC-MBE(2) scheme [ , 44, 1634-1644]. For a diverse test set of protein-ligand complexes, we demonstrate that by upgrading the MFCC scheme with many-body contributions, the error in protein-ligand interaction energies can be reduced significantly, and one generally achieves errors below 20 kJ/mol. Our scheme allows for systematically reducing these errors by including higher-order many-body contributions. As it combines the use of single amino acid fragments with high accuracy, our scheme provides an ideal starting point for the parametrization of accurate machine learning potentials for proteins and protein-ligand interactions.
量子化学碎片方法为准确计算蛋白质-配体相互作用能提供了一种有吸引力的方法。虽然带有共轭帽的分子分馏 (MFCC) 方案为此提供了一种相当直接的方法,但它的准确性通常不够。在这里,我们通过包含多体贡献来升级 MFCC 方案以计算蛋白质-配体相互作用。所得的碎片方案是我们之前开发的 MFCC-MBE(2) 方案的扩展[, 44, 1634-1644]。对于蛋白质-配体复合物的多样化测试集,我们证明通过用多体贡献升级 MFCC 方案,可以显著降低蛋白质-配体相互作用能中的误差,并且通常可以达到低于 20 kJ/mol 的误差。我们的方案通过包含更高阶的多体贡献,可以系统地降低这些误差。由于它结合了使用单个氨基酸片段的方法,并且具有高精度,因此我们的方案为蛋白质和蛋白质-配体相互作用的准确机器学习势的参数化提供了理想的起点。